Joining Case-Based Reasoning and Item-Based Collaborative Filtering in Recommender Systems

Abstract

Recommender systems can find user interested information based on the information filtering algorithms. Collaborative filtering technique has been proved to be one of the most successful techniques in recommender systems. And there are two approaches: one is user-based collaborative filtering and the other is item-based collaborative filtering. Data sparsity is the main problem in recommender system, which leads to the bad accuracy. To solve the sparsity problem, this paper presents a personalized recommendation algorithm joining case-based reasoning and item-based collaborative filtering. At first, it employs case-based reasoning technology to fill the vacant ratings of the user-item matrix. And then, it produces prediction of the target user to the target item using item-based collaborative filtering. The recommendation algorithm combining the case-based reasoning and item-based collaborative filtering can alleviate the sparsity issue and can produce more accuracy recommendation than the traditional recommender systems.